Pharmacology & Therapeutics 211 (2020) 107541

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Pharmacology & Therapeutics

j ourna l homepage: www.e lsev ie r .com/ locate /pharmthera

Physiologically-based pharmacokinetic models for children: Startingto reach maturation?

Laurens F.M. Verscheijden a, Jan B. Koenderink a, Trevor N. Johnson b, Saskia N. deWildt a,c, Frans G.M. Russel a,⁎a Department of Pharmacology and Toxicology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlandsb Certara UK Limited, Sheffield, UKc Intensive Care and Department of Paediatric Surgery, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands

Abbreviations:CYP, cytochrome P450; DDI, drug-drugtration rate; GST, glutathione s-transferase; kp, tissue-pmAB, monoclonal antibody; OAT, organic anion transpoporter; PBPK, Physiologically-based pharmacokinetic; UGT⁎ Corresponding author at: P.O. Box 9101, Geert Groo

128), 6500 HB Nijmegen, the Netherlands.E-mail address: Frans.Russel@radboudumc.nl (F.G.M.

https://doi.org/10.1016/j.pharmthera.2020.1075410163-7258/© 2020 The Author(s). Published by Elsevier I

a b s t r a c t

a r t i c l e i n f o

Available online 1 April 2020

Keywords:PediatricsChildrenOntogenyPBPKPhysiologically-based pharmacokineticmodelingModel-informed drug dosing

Developmental changes in children can affect the disposition and clinical effects of a drug, indicating that scalingan adult dose simply downper linearweight can potentially lead to overdosing, especially in very young children.Physiologically-based pharmacokinetic (PBPK) models are compartmental, mathematical models that can beused to predict plasma drug concentrations in pediatric populations and acquire insight into the influence ofage-dependent physiological differences on drug disposition. Pediatric PBPK models have generated attentionin the last decade, because physiological parameters for model building are increasingly available and regulatoryguidelines demand pediatric studies during drug development. Due to efforts from academia, PBPKmodel devel-opers, pharmaceutical companies and regulatory authorities, examples are now available where clinical studiesin children have been replaced or informed by PBPKmodels. However, the number of pediatric PBPKmodels andtheir predictive performance still lags behind that of adult models. In this reviewwe discuss the general pediatricPBPKmodel principles, indicate the challenges that can arise when developing models, and highlight new appli-cations, to give an overview of the current status and future perspective of pediatric PBPK modeling.

© 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Contents

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. Pediatric PBPK model development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33. Use of models to mechanistically describe ADME processes . . . . . . . . . . . . . . . . . . . . . . . . . . 34. 4 Exploratory pediatric PBPK models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85. Physiologically-based toxicokinetic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96. Quality control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97. Regulatory applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108. Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

interaction; GFR, glomerular fil-lasma partitioning coefficient;rter; OCT, organic cation trans-, UDP-glucuronosyltransferase..teplein 21, Room k0.10 (route

Russel).

nc. This is an open access article und

1. Introduction

New drugs require pediatric studies as part of their market authori-zation, while marketed drugs often lack information on pediatric effi-cacy, safety and dosing (EMA, 2007; FDA, 2002/2003; Frattarelli, et al.,2014; Sachs, Avant, Lee, Rodriguez, & Murphy, 2012). To find out whatdoses are suitable for different age groups, it is important to realizethat many developmental processes are not reflected by simple scalars

er the CC BY license (http://creativecommons.org/licenses/by/4.0/).

2 L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

like body weight or body surface area (Cella, Knibbe, Danhof, & DellaPasqua, 2010). Drug metabolizing enzyme or transporter activityshow protein-specific and organ-dependent developmental profiles(Daood, Tsai, Ahdab-Barmada, & Watchko, 2008; Lam et al., 2015;Mooij et al., 2014; Sadler et al., 2016; Upreti & Wahlstrom, 2016; vanGroen et al., 2018). Moreover, other processes involved in the disposi-tion of drugs also change in a nonlinear relationship with growth anddevelopment, as was reviewed recently (van den Anker, Reed,Allegaert, & Kearns, 2018).

Modeling and simulation has evolved as one of the cornerstones ofpediatric drug development by making optimal use of available data(Manolis et al., 2011). In population PK (popPK) models, parametersare computationally scaled/fitted in order to best describe in vivo mea-sured drug concentrations. In this approach patient-specific characteris-tics can be identified allowing for a more individualized therapy,although a robust estimation of population PK parameters requires rel-atively rich pharmacokinetic data, especially when multiplecharacteristics/co-variates are studied. Moreover, a PopPK parameterwill reflect a combination of several physiological and drug-related pro-cesses, which is difficult to extrapolate to other populations or drugs(Brussee et al., 2018; Brussee et al., 2018). PBPK models provide mech-anistic PK predictions and although the derivation of the required pa-rameters could be challenging, theoretically they are more suitable forbetween-population or between-drug PK predictions (W. Zhou et al.,2018; W. Zhou et al., 2016). Integrating developmental changes inPBPK models has proven to be successful in predicting doses acrossthe pediatric age span (Leong et al., 2012; Mansoor, Ahmad, AlamKhan, Sharib, & Mahmood, 2019). More specifically, regulatory author-ities have also acknowledged the added value of these models and

Lung

Adipose

Bone

Heart

Kidney

Muscle

Skin

Liver

Rest

Veno

us B

lood

Brain

IV

Qlu

Qbr

Qad

Qbo

Qhe

Qki

Qmu

Qsk

Qli

Qre

CLk

CLh

Dose PBPK modelling

Verification

Chi

-Plasma drug concentrations-Tissue/organ drug concentrations-Clinical effects (PD)

Conce

ntrati

on

Time

Trdes

param-Dose-Route of admin-Frequency of a

Vliver * dliver

dt =

Qa * Carterial + Qsp * ( Qsp

Kpsp * BP)

+ Qgu * ( Qgu

Kpgu * BP) - Qli * (

Qli

Kpli * BP)

- CLintmet * fuli * Cli

Application

Fig. 1. “Learn, confirm and apply” development cycle used to build and optimize pediatric PBPQbo, Qhe, Qki, Qmu, Qsk, Qsp, Qgu, Qha, Qre, Qli denote blood flows towards, lung, brain, atissues, and from liver, respectively (e.g. Qlu = 300 L ∗ h−1 in adults). Physiological (system) pin PBPK models.

encourage their use in pediatric drug development (Leong et al.,2012). The development of PBPK and/or PopPKmodels is in accordancewith FDA regulations (e.g. pediatric decision tree) as in any case pediat-ric drug pharmacokinetic (PK) and safety data need to be evaluated inorder to bridge from the adult to the pediatric population.

1.1. PBPK modeling in adults and children

PBPKmodels represent the body as anatomically and physiologicallyrecognizable compartments in which the processes of drug absorption,distribution,metabolism and excretion (ADME) are describedwith a setof differential equations. PBPK models provide a mechanistic frame-work that separately includes physiological parameters (often also re-ferred to as system specific parameters), drug-related parameters, andparameters reflecting trial design,which aims to cover the complex pro-cesses governing drug disposition (Fig. 1). Models range from a simpleminimal setup, consisting of only a few essential compartments, tofull-body PBPK models in which all major organs in the body are repre-sented by compartments connected through blood flow (Kuepfer et al.,2016; Upton, Foster, & Abuhelwa, 2016). Much progress has beenmadein accurately expressing the relevant physiological processes in terms ofaccurate parameters. While PBPK models usually consist of many pa-rameters and developing a model may be labor intensive, previousmodels can be used to build upon as physiological parameters are notexpected to change within a population of interest, whichmarkedly re-duces the effort that is needed for model building (Rostami-Hodjegan,2012). Next to the physiological parameters, a variety of drug-relateddata affecting pharmacokinetics need to be obtained, which largelycan be generated by in vitro experiments (Fig. 1).

Spleen

Gut

Art

eria

l Blo

od

Oral

Qlu

Qbr

Qad

Qbo

Qhe

Qki

Qmu

Qsk

Qsp

Qha

Qre

Qgu

Qsp

Qgu

Drug-related parameters

(Age-related)physiological parameters

ld

-Transporter abundance-Enzyme abundance-Organ volumes-Blood flows-Glomerular filtration

-Transport rate-Enzymatic conversion rate -Lipophilicity-Protein binding

Expres

sion

Age

ial igneters

istrationdministration

K models based on physiological, drug-related and trial design parameters. Qlu, Qbr, Qad,dipose tissue, bone, heart, kidney, muscle, skin, spleen, gut, liver (arterial flow), rest ofarameters, drug-related parameters and trial-design parameters are separately included

3L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

Models can be adjusted to other populations by changing thepopulation-dependent system parameters. They have successfullybeen translated from animals to humans (Ball, Bouzom, Scherrmann,Walther, & Decleves, 2012; Bi, Deng, Murry, & An, 2016; Lukacovaet al., 2016; Parrott et al., 2011), from Caucasians to different ethnicities(Feng et al., 2016;Matsumoto et al., 2018), and fromhealthy to diseasedpopulations (Radke et al., 2017; Rasool, Khalil, & Laer, 2015; Rhee,Chung, Yi, Yu, & Chung, 2017). Similarly, extrapolations from adults tochildren are based on adapting age-dependent adult physiological pa-rameters to values appropriate for children. This approach is commonlyapplied in thedevelopment of pediatricmodels to evaluate key pharma-cokinetic processes and confirm age-unrelated drug-dependent param-eters in an adult population, before introducing age-related pediatricphysiological parameters, for which data can be more sparse (Maharaj& Edginton, 2014).

Pediatric PBPKmodeling has developed from ‘proof of principle’ to avaluable tool for the prediction of pharmacokinetics in children(Yellepeddi et al., 2018). Even more, PBPK-PD (PBPK-pharmacody-namic) modeling is increasingly used to predict drug effects. The in-creased interest is also reflected by an exponential rise in the numberof publications on this topic during the last decade. On the other hand,the number of pediatric PBPK models and their predictive performancestill lags behind adultmodels (Grimstein et al., 2019; Jamei, 2016; Sager,Yu, Ragueneau-Majlessi, & Isoherranen, 2015; Templeton, Jones, &Musib, 2018). The aim of this review is to (1) discuss the use of pediatricPBPK models for different purposes and identify challenges in modelbuilding, (2) provide key considerations to evaluate pediatric PBPKmodel quality, and (3) to give a future perspective on model develop-ment that will further increase their quality and acceptance, as well astheir wider applicability into clinical care.

2. Pediatric PBPK model development

A tutorial for a general workflow to develop a pediatric PBPK modelwas described by Maharaj et al. and will not be further discussed here(Maharaj, Barrett, & Edginton, 2013;Maharaj & Edginton, 2014). Trendsbetween adult and pediatric physiological parameters are summarizedin Table 1.

In general, it is considered good practice to develop an adult modelfirst before a pediatric model is built, to obtain insight into key pharma-cokinetic processes and allow for the verification of age-independentdrug-related parameters. Some of the physiological parameters thatare subsequently included in the pediatric model are well established,such as organ volumes, however, information on others can be sparseor absent (e.g. transporter expression). This indicates that dependenton the route of administration (e.g. oral versus intravenous dosing)and the drug involved (e.g. CYP3A4 substrate versusUGT substrate) con-fidence in the model-predicted outcomes will be determined by the(un)certainty of the estimates for the included parameters. Althoughimportant information gaps may exist in ADME-related physiology forspecific drug models, a pediatric PBPK model can be judged ‘fit for pur-pose’ if the relevant patterns related to age can be described and suffi-ciently verified with clinical data.

For pediatric model development, it is valuable to obtain PBPK drug-specific parameters from human in vitro studies that can be scaled toin vivo parameters (in vitro-in vivo extrapolations (IVIVE), or also called“bottom up approach”). For example, an in vitro clearance value can becalculated from recombinant drugmetabolizing enzyme activity, whichis subsequently scaled to whole liver clearance by taking into accountage-appropriate liver weight and enzyme expression per gram. Suchan approach can contribute to the development of first in child dosingregimens in case it is not possible to scale or fit parameters based oncomparison of predicted model output with measured drug concentra-tions. In addition, it results in better mechanistic insight into the under-lying ADME processes, for example the relative contribution ofindividual drug metabolizing enzymes in clearance (Jaroch, Jaroch, &

Bojko, 2018; Johnson et al., 2018; Scotcher, Jones, Posada, Rostami-Hodjegan, & Galetin, 2016).

In practice, a combination of in vitro data and in vivo-derived drugconcentrations are often used for model parametrization. If physiologi-cal or drug-related parameters for PBPK models are not available, theyneed to be scaled or fitted based on clinically measured drug concentra-tion data. This “middle out approach” allows the quantification of pro-cesses affecting PK and to explore potential differences betweenadults and children (Emoto, Johnson, McPhail, Vinks, & Fukuda, 2018;Zane & Thakker, 2014). Similarly, pediatric models will also benefitfrom adult in vivo pharmacokinetic data. For instance, adult clearancevalues can be used to estimate pediatric clearances if differences in en-zyme expression and activity of the elimination pathways involved aretaken into consideration.

3. Use of models to mechanistically describe ADME processes

3.1. Absorption

Oral dosing is the preferred route of drug administration in children.Multi-compartment absorptionmodels are used to predict drug absorp-tion from different gut segments, in which the complex interplay be-tween different physiological processes and their effect on absorptionis incorporated. Age-related processes accounted for oral absorption inPBPKmodels are gastric emptying time, small and large intestinal tran-sit time and intestinal surface area,which are only part of the physiolog-ical processes subject to developmental differences (Table 2). Recently,a gastro-intestinal model was built by Johnson et al. based on a reviewof the literature. They recognized knowledge gaps in the ontogeny offluid volume dynamics in the GI tract, intestinal bile flows, and CYP en-zyme and transporter expression. Nevertheless, disposition of the rela-tively high solubility and permeability drugs, paracetamol andtheophylline, were predicted with good precision. In addition, accuratepredictionswere alsomade for the low solubility drug ketoconazole andcarbamazepine. (Cristofoletti, Charoo, & Dressman, 2016; Johnson,Bonner, Tucker, Turner, & Jamei, 2018; Kohlmann, Stillhart, Kuentz, &Parrott, 2017) (Table 2).

Intestinal protein ontogeny data for CYP3A4 show a developmentalincrease in activitywhen childrenmature, whereas expression of the ef-flux transporter P-glycoprotein appears to be stable from fetal age untiladulthood (Table 1, Fig. 2) (Johnson, Tanner, Taylor, & Tucker, 2001;Konieczna et al., 2011). Knowledge on intestinal abundance of otherdrug metabolizing enzymes and transporters is still limited. In thatcase PBPK models are useful in combination with measured clinicaldrug concentrations to explore developmental differences in enzymeand transporter expressions that have not yet been characterized atthe protein level.

Models describing other routes of absorption such as dermal, pulmo-nary and ocular drug absorption were developed previously for adults,rodents and rabbits, however, for children they are scarce (Le Merdyet al., 2019; Poet et al., 2000; Salar-Behzadi et al., 2017; Valcke &Krishnan, 2010). One study describedmulti-route (oral, dermal, pulmo-nary) exposure to drinking water toxicants in neonates and children,providing proof of principle also for other xenobiotics including drugs(Valcke & Krishnan, 2010).

3.2. Distribution

Once in the systemic circulation, a drug will be distributed to organsand tissues, which is usually described in PBPK models by the (pre-dicted) tissue-plasma partitioning coefficient (Kp) (Table 3)(Poulin &Theil, 2000; Rodgers, Leahy, & Rowland, 2005; Rodgers & Rowland,2006). Age-appropriate calculations of the Kp value of a drug is depen-dent on the fractional volumes of tissue water and lipid, as well as frac-tion of the compoundwhich is unbound in plasma. In general, neonatesand young children will have a higher percentage of tissue water and

Table 1Developmental trends in physiological parameters.

ADMEprocess

Physiologicalparameter

Developmental pattern Age range reported Ref.

Absorption Small intestinallengtha

Increase Fetuses-adult (Gondolesi et al., 2012; Struijs, Diamond, de Silva, & Wales, 2009; Weaver, Austin, & Cole,1991)

Large intestinallengtha

Increase Neonates-adolescents (Koppen et al., 2017; Mirjalili, Tarr, & Stringer, 2017)

Gastric pH Stable, subject toalkalinization by milkfeeds

Neonates-adolescents (Avery, Randolph, & Weaver, 1966; Schmidt et al., 2015; Whetstine, Hulsey, Annibale, &Pittard, 1995)

Small intestinal pH Stable Neonates-children (Barbero et al., 1952; Fallingborg et al., 1990)Gastric emptying Stable Neonates-adults (Bonner et al., 2015)Gut transit time Stable Neonates- adults (Maharaj & Edginton, 2016)Intestinalmembranetransporters

Fetuses-adults (Konieczna et al., 2011; Mizuno et al., 2014; Mooij et al., 2014)

- Pgpb,c Stable- BCRPc Stable- MRP1c Stable- MRP2b Stable- OATP-2B1b Decrease

Intestinal drugmetabolizingenzyme

Fetuses-adults (Fakhoury et al., 2005; Johnson et al., 2001)

- CYP3A4c IncreaseDistribution Tissue composition Fetuses-children

2 years of age(Butte, Hopkinson, Wong, Smith, & Ellis, 2000; Carberry, Colditz, & Lingwood, 2010;Malina, 1969)- Proteind Stable

- Waterd Decrease- Fatd Increase

Organ volumesa Increase Neonates-adults (Ogiu, Nakamura, Ijiri, Hiraiwa, & Ogiu, 1997)Organ blood flow Neonates-adults (Chiron et al., 1992; Schoning & Hartig, 1996; Williams & Leggett, 1989)- Braina First increase, later

decrease- Other organsa Increase

Carrier proteins Neonates- children3 years of age

(Johnson et al., 2006; Kanakoudi et al., 1995; Maharaj, Gonzalez, Cohen-Wolkowiez,Hornik, & Edginton, 2018; Sethi et al., 2016)- Albumine Increase

- A1AGPe IncreaseHematocritf First decrease, later

increase(Fulgoni III et al., 2019; Jopling, Henry, Wiedmeier, & Christensen, 2009)

Metabolism Liver enzymeexpression

Fetuses- children2 years of age

(Bhatt et al., 2019; Divakaran, Hines, & McCarver, 2014; Johnson et al., 2006; Salem et al.,2014; Song et al., 2017; Upreti & Wahlstrom, 2016; Zaya, Hines, & Stevens, 2006)

CYP1A2c IncreaseCYP2B6c IncreaseCYP2C8c IncreaseCYP2C9c IncreaseCYP2C19c IncreaseCYP2D6c IncreaseCYP2E1c IncreaseCYP3A4c IncreaseUGT1A1c IncreaseUGT1A4c IncreaseUGT1A6c IncreaseUGT1A9c IncreaseUGT2B7c IncreaseUGT2B15c IncreaseHepatictransporterexpression

Fetuses-adults (Mooij et al., 2016; Prasad et al., 2016; van Groen et al., 2018)

- Pgpc Increase- BCRPc Stable- MRP1c Increase- MRP2c Stable/increase- MRP3c Increase- BSEPc Stable/increase- NTCPc Increase- OATP-1B1c Stable- OATP-1B3c Stable/increase- OATP-2B1c Stable- OCT1c Increase

Microsomalproteine

Increase Neonates-adults (Barter et al., 2008)

Elimination Glomerularfiltration ratea

Increase Fetuses-adults (Hayton, 2000; Johnson et al., 2006; Piepsz, Tondeur, & Ham, 2006; Rhodin et al., 2009)

Tubulartransporterexpression

Neonates-adults (Cheung, van Groen, Spaans, et al., 2019)

- Pgpc Increase

4 L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

Table 1 (continued)

ADMEprocess

Physiologicalparameter

Developmental pattern Age range reported Ref.

- BCRPc Stable- MATE1c Stable- MATE2-kc Stable- URAT1c Stable- GLUT2c Stable- OAT1c Increase- OAT3c Increase- OCT2c Increase

Pgp, P-glycoprotein; BCRP, Breast cancer resistance protein; MRP, Multidrug resistance protein; OATP, Organic anion transporter protein; CYP, Cytochrome P450; A1AGP, Alpha-1-acidglycoprotein; UGT, Uridine diphosphate-glucuronyltransferase; BSEP, Bile salt export pump; NTCP, Sodium-taurocholate cotransporting polypeptide; OCT, Organic cation transporter;MATE, Multidrug and toxin extrusion; URAT, Urate transporter; GLUT, Glucose transporter; OAT, Organic anion transporter.

a Absolute length, volume, flow or rate.b mRNA expression.c Protein expression.d Percentage of body weight.e Concentration.f Percentage of blood volume.

Table 2PBPK models including prediction of oral absorption.

Study Parameterfitting/optimizationneeded?

Pediatric systems parameters included Softwareused

Age range Drug Ref.

Parrott et al. No Gut size, intestinal transit time Gastroplus® Neonate, infant Oseltamivir (Parrott et al.,2011)

Johnson et al. Yes GI tract size, CYP3A4 ontogeny Simcyp® Quetiapine (Johnson et al.,2014)

Khalil et al. Yes Radius and length of intestinal segments, effectivesurface area intestinal sections, intestinal enzymeontogeny

Simcyp®andPK-Sim®

11d–17.7y Sotalol (Khalil & Laer,2014)

Willman et al. Yes Gastric emptying time, small and large intestinal transittime, effective surface area intestinal sections

PK-Sim® 0.5–18y Rivaroxaban (Willmann et al.,2014; Willmannet al., 2018)

Rasool et al. Yes Not stated Simcyp® 0.12–19.3y Carvedilol (Rasool et al.,2015)

Cristofolettiet al.

Yes Intestinal volumes, bile salt concentration, Gastric pH Simcyp® Fluconazole,Ketoconazole

(Cristofolettiet al., 2016)

Villiger et al. Yes Intestinal length, intestinal surface area, small intestinaltransit time, fluid secretion volume

Gastroplus® Newborns, infants,children

Sotalol,Paracetamol

(Villiger, Stillhart,Parrott, & Kuentz,2016)

Moj et al. Yes Not stated PK-Sim® 0–17y Vorinostat (Moj et al., 2017)Kohlman et al. No Gut size and GI transit times Gastroplus® Newborns-adolescents Carbamazepine (Kohlmann et al.,

2017)Samant et al. No Not stated Gastroplus® 0–22y Desipramine (Samant et al.,

2017)Johnson et al. No Gastric emptying, gastric and intestinal pH, intestinal

length and diameter, intestinal transit time, salivaryflow rates, gastric and intestinal volumes, intestinal bilesalt concentration

Simcyp® 0–25y Theophylline,Paracetamol,Ketoconazole

(Johnson, Bonner,et al., 2018)

Balbas-Martinezet al.

Yes Not stated PK-Sim® 3 m-12y Ciprofloxacin (Balbas-Martinezet al., 2019)

5L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

lower plasma albumin and alpha 1-acid glycoprotein concentrationscompared to adults (Table 1), which is reflected in altered Kp values.These age-related changes may consequently result for a specific drugin a different predicted volume of distribution per kg body weight(Samant, Lukacova, & Schmidt, 2017).

Drug disposition into organs can also be described by diffusion-limited compartments. This mainly will be useful in cases where de-layed drug penetration into organs is expected and/or transporter-mediated transfer of compounds across cell membranes needs to beaccounted for. Data on maturation of drug transporters in the differentorgans are, however, limited and lagging behind the knowledge onthe expression profiles of drug metabolizing enzymes (Table 1). Proofof principle for this approach was given in a study where OCT1-mediated liver uptake of morphine was included in a pediatric PBPK

model, which could be verified by comparison of predicted and mea-sured clearance values (Emoto et al., 2018). Because morphine is ahigh extraction drug, clearance ismainly dependent onmorphine deliv-ery to the hepatocytes, which is influenced by hepatic blood flow andOCT1-mediated liver uptake, and to a lesser extent by UGT2B7 activity(Emoto, Johnson, Neuhoff, et al., 2018). First, OCT1 genotypewas inves-tigated as a source of variation in morphine liver uptake in adults andchildren older than 6 years of age (Emoto et al., 2017). Subsequently,ontogeny of OCT1 and an optimized relation between cardiac outputand age, which influences hepatic blood flow, were included into thepediatric PBPK model to describe the clearance in neonates and younginfants (Emoto et al., 2017; Emoto, Johnson, Neuhoff, et al., 2018). In afollow-up study, the ontogeny of UGT2B7 expression was also includedin the modeling (Bhatt et al., 2019).

CYP1A2

CYP3A4CYP2D6

Liver enzyme ac�vity

Ac�v

ity (%

of a

dult)

0 50 150 200

Postmenstrual age (weeks)

0

50

100

150

100 250

Expr

essio

n (%

of a

dult)

Postnatal age (weeks)

Kidney transporter expression

0

50

100

Expr

essio

n (%

of a

dult)

Brain transporter expression

GA 20-26 wk

GA 36-40 wk

PNA 0-3 mnd

PNA 3-6 mnd

Adult

Expr

essio

n/Ac

�vity

(% o

f ado

lesc

ent)

25

75

0

50

100

25

75

0

50

100

25

75

Fetus

Neonate

PNA 3 mnd- 2

yrs

PNA 2-5 yrs

PNA 5-12 yrs

PNA >12 yrs

Intes�ne transporter expression /enzyme ac�vity

ND

0 200 400 600

0

50

100

25

75

Expr

essio

n (%

of a

dult)

0 50 150 200100 250Postnatal age (weeks)

Liver transporter expression

CYP3A4Pgp

PgpOCT1OATP1B3

OCT2PgpOAT1OAT3

Pgp

6 L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

3.3. Metabolism

Most efforts have been directed at incorporating age-dependentchanges in metabolic clearance into pediatric PBPK models (Fig. 2,Table 4). Data from in vitro assays (i.e. recombinant enzymes, humanliver S9 fractions, human liver microsomes, or human hepatocytes)have been used to estimate hepatic clearance in adult models, whichcan also be applied to pediatric models by taking into account reporteddifferences in enzyme expression/activity. Investigations on the expres-sion ontogeny of hepatic CYP enzymes have resulted in accurate predic-tions for children down to an age of 1 month for drug metabolismcovered by the enzymes CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6and CYP3A4 (Table 4) (W. Zhou et al., 2018). An in vitro study on the on-togeny profiles of different CYP enzyme activities, showed that for drugshandled by CYP1A2 and CYP3A4 these values resulted in an underesti-mation of the in vivo clearance in the pediatric age range (Johnson,Rostami-Hodjegan, & Tucker, 2006; Salem, Johnson, Abduljalil, Tucker,& Rostami-Hodjegan, 2014; Upreti & Wahlstrom, 2016). By using clini-cally determined clearance values of midazolam and sufentanil (bothCYP3A4), theophylline and caffeine (both CYP1A2), the ontogeny forthese enzymes was further refined (Salem et al., 2014). Subsequently,this was verified with clearance values for alfentanil (CYP3A4) andropivacaine (CYP1A2) (Salem et al., 2014). A major drawback is thatin vivo clearance data were obtained in ill children, which potentiallyhas affected enzyme activity, but a good correlation between predictedand measured clearance values was found (Salem et al., 2014; Upreti &Wahlstrom, 2016). Although CYP enzyme developmental patterns arerelatively well described, in several situations prediction of clearancewas complicated by the absence of maturation profiles of metabolizingenzymes. Studies have been published describingmodels incorporatingGST- and UGT-mediated clearance, in which only theoretical functions(i.e. not based on observed data) were used to describe enzyme ontog-eny, for instance based on other isoenzymes or fitting tomeasured drugconcentrations.While this indicates that prediction for these enzymes ismore difficult, these efforts aid in further establishing their ontogenyprofiles. (Diestelhorst et al., 2014; Jiang, Zhao, Barrett, Lesko, &Schmidt, 2013).

In neonatal and preterm models, measured drug concentration areless well predicted by PBPK models compared to older children andadults (Khalil & Laer, 2014; Templeton et al., 2018; T'Jollyn,Vermeulen, & Van Bocxlaer, 2018). Recently, developmental physiolog-ical parameters in the preterm/neonatal population were re-evaluatedto provide a bettermechanistic basis for this age group and drug plasmaconcentrations for six drugs were accurately described (Abduljalil, Pan,Pansari, Jamei, & Johnson, 2019a, 2019b). Another aspect consideredspecifically in neonates is that system-specific parameters can changerapidly over a relatively short period of time. In PBPK models parame-ters usually are fixed for a “virtual individual” during the time courseof the simulation. However, to account for time varying physiology inneonates, in other words, to include growth and/or maturation in vir-tual individuals, a model has been developed in which the values forphysiological parameters are re-defined during the time course of the(prolonged) simulation (Abduljalil, Jamei, Rostami-Hodjegan, &

Fig. 2. Developmental patterns in enzyme and transporter expression or activity inintestine, liver, kidney and brain. Solid lines indicate ontogeny profiles for which age-related equations are described. Dotted lines with point estimates indicate expression/activity levels at specific age groups. Refs: Intestine (Johnson et al., 2001; Koniecznaet al., 2011), liver (enzymes) (Salem et al., 2014; Upreti & Wahlstrom, 2016), liver(transporters) (Prasad et al., 2016), kidney (Cheung, van Groen, Spaans, et al., 2019),brain (Lam et al., 2015). Pgp, P-glycoprotein; CYP, Cytochrome P450; OCT, Organiccation transporter; OAT, Organic anion transporter.

Table 3PBPK models including prediction of drug distribution.

Study Permeability-limitedcompartmentsincluded

Estimation methodtissue-plasma partitioningcoefficients (perfusion-limitedcompartments)

Softwareused

Age range Drug Ref.

Samant et al. No Lucacova Gastroplus® 0–15 years Desipramine (Samant et al., 2017)Emoto et al. Liver Rodgers and Rowland Simcyp® 0–3 years Morphine (Emoto, Johnson,

Neuhoff, et al., 2018)Verscheijdenet al.

Brain Rodgers and Rowland Rstudio® 0.25–15 years andadults

Paracetamol, naproxen, flurbiprofen,ibuprofen, meropenem

(Verscheijden et al.,2019)

Maharajet al.

Brain Rodgers and Rowland PK-Sim® 0 years - adult Lorazepam (Maharaj et al., 2013)

Lukacovaet al.

All tissues Not applicable Gastroplus® 11 days - 17 yearsand adults

Ganciclovir, valganciclovir (Lukacova et al., 2016)

Table 4PBPK models including prediction of metabolic elimination.

Study Enzyme ontogeny Softwareused

Agerange

Drug Ref.

Yun et al. CYP1A2, CYP2B6, CYP2C9, CYP2C19,CYPD6, CYP2E1, CYP3A4

PK-Sim® 1 week -adult

Alfentanil, diclofenac, esomeprazole, itraconazole, lansoprazole, midazolam,ondansetron, sufentanil, theophylline, tramadol

(Yun & Edginton,2019)

Zhou et al. CYP1A2, CYP2C8, CYP2C9, CYP2C19,CYP2D6, CYP3A4

Simcyp® 1 month- adult

Theophylline, desloratidine, montelukast, diclofenac, esomeprazole,lansoprazole, tramadol, itraconazole, ondansetron, sufentanil

(W. Zhou et al.,2018)

Salem et al. CYP1A2, CYP3A4 Simcyp® 1 day -adult

Caffeine, theophylline, midazolam, ropivacaine, alfentanil (Salem et al.,2014)

Upreti andWahlstrom

CYP1A2, CYP2A6, CYP2B6, CYP2C8,CYP2C9, CYP2C19, CYP2D6, CYP2E1,CYP3A

Simcyp® 1 day -adult

Caffeine, cotinine, nicotine, cyclophosphamide, methadone, phenytoin,tolbutamide, omeprazole, pantoprazole, propafenone, sevofluorane,sufentanil, midazolam, sildenafil, theophylline, S-warfarin, alfentanil,montelukast, efavirenz, lansoprazole, metronidazole, nevirapine,pantoprazole, ropivocaine

(Upreti &Wahlstrom,2016)

Johnsonet al.

CYP1A2, CYP2B6, CYP2C8, CYP2C9,CYP2C18/19, CYP2D6, CYP2E1,CYP3A4/5,

Simcyp® 1 day -adult

Midazolam, caffeine, diclofenac, omeprazole, cisapride, carbamazepine,theophylline, phenytoin, S-warfarin

(Johnson et al.,2006)

Edgintonet al.

CYP1A2, CYP2E1, CYP3A, SULT,UGT1A1, UGT1A1, UGT1A6, UGT1A9,UGT2B7

PK-Sim® 1 day -adult

Paracetamol, alfentanil, morphine, theophylline, levofloxacin (Edginton,Schmitt, &Willmann, 2006)

Bhatt et al. UGT2B7 Simcyp® 1 day -adult

Morphine, zidovudine (Bhatt et al.,2019)

Jiang et al. CYP1A2, CYP2C9, CYP2C19, CYP2D6,CYP2E1, CYP3A4, SULT, UGT1A1,UGT1A9, UGT2B15

Simcyp® 1 day -adult

Paracetamol (Jiang et al.,2013)

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Johnson, 2014). In this model, parameters involved in metabolic clear-ance e.g. CYP3A4 and CYP2C9 expression and liver weight were in-cluded, which resulted in better correlations between predicted andmeasured data for sildenafil (Abduljalil et al., 2014).

Finally, in case the relevant ontogeny profiles in children are ro-bustly described in PBPK models, more complex age-related variationin drug metabolism may be detected. For example, a change in relativeenzyme contribution during growth, as has been described for paracet-amol and sirolimus, or a change in relative contribution of eliminatingorgans, as described for caffeine (Filler, 2007; Mooij et al., 2017; Ponset al., 1988).

3.4. Excretion

Renal excretion of drugs depends on (1) freely filtered drug that isdetermined by glomerular filtration rate and protein binding, (2) tubu-lar secretion, and (3) tubular reabsorption. Equations describing ontog-eny profiles for glomerular filtration rate (GFR) have been reported inmultiple studies and used to estimate the amount of drug that is freelyfiltered (Duan et al., 2017; Johnson et al., 2006; Rhodin et al., 2009;Schwartz et al., 1976). In recently described models, GFR is predictedbased on ontogeny functions derived from inulin and 51CR-EDTA mea-surements, which gives more accurate results than using the creatinineclearance (Johnson et al., 2006; Rhodin et al., 2009). Inclusion of tubularsecretion and absorption via transporter-mediated processes has

lagged behind in pediatric applications, as data on human membranetransporter ontogeny were, until recently, very scarce (Table 5,Fig. 2) (Cheung et al., 2019). By measuring the in vivo renal clearanceof the P-glycoprotein substrate digoxin over a broad age range, thecontribution of tubular secretion in children was used as a surrogatemarker for the transporter's ontogeny profile (Willmann et al., 2014).This was done by subtracting the age-related GFR-mediated clearancefrom total digoxin clearance, which enabled simulation ofrivaroxaban plasma concentrations (another P-glycoprotein sub-strate) over the pediatric age range. In this case the authors assumedthat P-glycoprotein transport is the rate-limiting factor in tubularsubstrate secretion.

In case the transporter involved is unknown, or no transporter-specific substrate data is available to estimate transporter-mediated ab-sorption or secretion, the ratio of GFRpediatric/GFRadult has been used as asurrogate for pediatric renal clearance. However, this assumes thatmat-urational processes in transporters are paralleling the development ofGFR, which is often not the case (Cheung, van Groen, Spaans, et al.,2019; Duan et al., 2017; Johnson et al., 2006; Rhodin et al., 2009). Thelimitation of this assumption is also exemplified by a PBPK study inwhich thismethodwas used to scale pediatric clearance for nine renallycleared drugs. Although for most children acceptable predictions wereobtained, a trend towards an overestimation of renal clearance wasfound for children b2 years of age, indicating that physiological pro-cesses that affect renal clearance differ quantitatively between adults

8 L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

and young children (W. Zhou et al., 2016). This might be explained bylower renal proximal tubular transporter expression of P-glycoprotein(apical), organic anion transporter (OAT)1, OAT3 and organic cationtransporter (OCT)2 (basolateral) in neonates and young children(Cheung, van Groen, Spaans, et al., 2019). The largest overpredictionof renal clearance was observed for vancomycin, which is transportedby OCT2 (Sokol, 1991; W. Zhou et al., 2016). If urinary excretion of adrug is of minor influence on its total body clearance, ontogeny ofrenal clearance is often ignored inmodeling (Walsh et al., 2016).Model-ing the clearance of renally excreted drugs hence awaits inclusion ofkidney transporter ontogeny and a means to scale in vitro to in vivotransport rates. A recent study indicates that this indeed leads to betterpredictions (Cheung et al., 2019). To our knowledge pediatric modelsincluding age-appropriate elimination via other routes such as bile, ex-halation and sweat glands are not yet reported.

4. 4 Exploratory pediatric PBPK models

4.1. Predictions of (target) tissue concentrations

Most PBPK models are used to describe plasma concentration-timeprofiles, but due to their compartmental structure, tissue concentrationscan be predicted aswell, which likely correlate better with the pharma-cological/toxicological effects (Gerard et al., 2010; Hornik et al., 2017).Especially concentrations in brain are of interest, as this organ isprotected by the blood-brain barrier equipped with multiple drug up-take and efflux transporters. For lipophilic drugs, relatively simpleblood flow-limited models might be sufficient due to the rapid transferof the drug into the brain and predictions can be done by calculatingbrain-plasma partitioning coefficients (Alqahtani & Kaddoumi, 2016;Donovan, Abduljalil, Cryan, Boylan, & Griffin, 2018). These models arenot suitable for more polar drugs, where low BBB permeability restrictsbrain access resulting in low exposure levels and a lag-time betweenplasma and brain concentrations. One group started with apermeability-limited rat model that was adjusted for adult humans.This model was further refined to allow predictions on brain morphineextracellular fluid concentrations in children between 3 and 13 years ofage (Ketharanathan et al., 2018; Yamamoto et al., 2017; Yamamotoet al., 2018). In another study, an adult PBPK-CSF model was extrapo-lated to children between 3 months and 15 years of age and verifiedwith multiple drug CSF concentrations (Verscheijden, Koenderink, deWildt, & Russel, 2019). Both groups did not specifically consider the in-fluence of age on brain transporter expression, which is of main interestfor further studies, as accumulating evidence indicates that at least P-glycoprotein activity appears age-related in the pediatric population(Nicolas & de Lange, 2019). Tissue concentrations in other organs havebeen described using simple Kp-based predictions, for example inskin, bone and lung (Hornik et al., 2017; Ogungbenro, Aarons, Cresim,& Epi, 2015; Thompson et al., 2019).

To date, models for the estimation of tissue concentrations havebeen more exploratory in nature as compared to models for predictingthe course of the plasma drug concentration. This is due to limited ac-cess to tissue drug concentrations for verification, knowledge about dis-position between different parts of an organ, and on organ transporterontogeny. For a better applicability of thesemodels, more studies are re-quired to obtain mechanistic information on the processes that governtissue exposure.

4.2. Prediction of fetal tissue concentrations

By linking fetal compartments to a maternal PBPK model, combinedmaternal and fetal ADME processes can be described. Maternalpregnancy-induced changes have been reported for volume of distribu-tion, enzyme activity, blood flows, and plasma albumin and alpha-acidglycoprotein concentrations (Abduljalil, Furness, Johnson, Rostami-Hodjegan, & Soltani, 2012). For the fetal model, physiological

parameters and their developmental pattern are needed to predictdrug exposure (Abduljalil, Jamei, & Johnson, 2019; Ke, Greupink, &Abduljalil, 2018). Recently, more data has become available for ontog-eny of physiological parameters in the fetus, for instance concerning de-velopmental patterns in organ volumes (Abduljalil, Jamei, et al., 2019;Abduljalil, Johnson, & Rostami-Hodjegan, 2018; Zhang et al., 2017).

Quantitative information on drug transfer from the maternal side tothe fetal compartments can be obtained from clearance studies in theisolated perfused human placental cotyledon, or by measuring drugtransfer over a cell line monolayer (De Sousa Mendes et al., 2017;Schalkwijk et al., 2018; Zhang et al., 2017; Zhang & Unadkat, 2017). Pla-cental perfusion experiments are usually performed with normal termplacentas, which means that transporter and enzyme expression arelikely to be different at a lower gestational age, making extrapolationto earlier stages of pregnancy difficult. Experimentswith cell linemono-layers suffer from similar problems, as quantification of transporter ex-pression is needed for the complete gestational age range to correct forthe differences in abundance between fetal placenta and the cell systemused for predictions (Ke et al., 2018). In addition, parameter optimiza-tion and model verification are challenging as for obvious reasons fetaldrug exposure data at an early gestational age may be hard to acquire.Model verification of fetal compartments with cord blood is an impor-tant source of data, although samples are only available at birth and dif-ferences in timing between the last dose and sampling introducesvariability in the concentrations measured. Also single measurementsdo not give information on the underlying fetal concentration-time pro-files (Schalkwijk et al., 2018).

4.3. Predictions for monoclonal antibodies

Therapeutic use of monoclonal antibodies (mAbs) and large proteinmolecules has grown rapidly over the years. Although their body dispo-sition is rather different from that of small molecule drugs, PBPKmodelscan also be valuable in predicting the pharmacokinetics of biologicals(Gill, Gardner, Li, & Jamei, 2016).Models formAbs require incorporationof different physiological processes, compared to small molecules, suchas lysosomal degradation, lymph flows, endogenous antibody concen-trations, and FcRn receptor-mediated recycling, which also show age-related variation (Edlund, Melin, Parra-Guillen, & Kloft, 2015; Jones,Mayawala, & Poulin, 2013; Malik & Edginton, 2018). Data is not yetavailable for all processes affecting pharmacokinetics of mAbs, whichunderscores the need for studies unraveling these developmental phys-iological parameters. Nevertheless, an attempt was made to scale anadult PBPK model to a pediatric variant for the therapeutic monoclonalIgG antibodies bevacizumab and palivizumab and although many pa-rameters were uncertain, this approach can be used as a frameworkfor building a generic pediatric PBPK model that captures all complexi-ties (Hardiansyah & Ng, 2018).

4.4. Determine effects of non-maturational factors

In addition to age-related variation in ADMEprocesses, PBPKmodelsare very well suited to explore and/or incorporate the effect of non-maturational factors, such as disease, genetics and drug-drug interac-tions (DDIs) on pharmacokinetics (Zakaria & Badhan, 2018). For exam-ple, depending on the maturation profiles of the proteins involved in aDDI, the magnitude of interaction may be different in various agegroups, which indicates that information on enzyme/transporter pro-tein ontogeny, and their effects on the interaction needs to be described(Salem, Johnson, Barter, Leeder, & Rostami-Hodjegan, 2013). Simula-tions of DDIs are not yet common practice in pediatric populations, al-though some papers have been published, mainly on CYP3A4 (A. Li,Yeo, Welty, & Rong, 2018; Ogungbenro, Aarons, & Cresim, & Epi, C. P.G., 2015; Olafuyi, Coleman, & Badhan, 2017). In these studies, no verifi-cation was performed in children b 2y of age, in which developmentaldifferences in CYP3A4 expression are expected to have the largest

Table 5PBPK models including prediction of renal elimination.

Study GFR ontogeny Tubular secretion/absorption ontogeny Softwareused

Age range Drug Ref.

Balbas-Martinez Rhodinontogeny

p-Aminohippuric acid ontogeny PK-Sim® 3 m-12y Ciprofloxacin (Balbas-Martinez et al., 2019)

Lukacova et al. PEAR moduleontogeny

Optimized to measured data Adulttransporter expression levels

Gastroplus® 11 days – 17 yearsand adults

Ganciclovir,valganciclovir

(Lukacova et al., 2016)

Zhou et al. Johnsonontogeny

Same as GFR ontogeny Simcyp® Neonates- adults Nine renallycleared drugs

(W. Zhou et al., 2016)

Walsh et al. NA NA Simcyp® 0–12 months andadults

Actinomycin D (Walsh et al., 2016)

Parrott et al. 1/10th of adult 1/10th of adult Gastroplus® Neonates, younginfants, adults

Oseltamivir (Parrott et al., 2011)

Duan et al. Johnsonontogeny

Same as GFR ontogeny Simcyp® 1 day – 17 years Linezolid,emtricitabine

(Duan et al., 2017)

Willmann et al. Rhodinontogeny

Digoxin tubular secretion (P-glycoprotein)ontogeny

PK-Sim® Neonates-adolescents Rivaroxaban (Willmann et al., 2014)

Cheung et al. Not stated Kidney developmental drug transporterexpression data

Not stated 0–7 years and adults Tazobactam (Cheung, van Groen, Burckart,et al., 2019)

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impact on the difference in DDI magnitude compared to adults. Diseaseeffects have been incorporated in adult models for patients with im-paired kidney (Yee et al., 2017; L. Zhou et al., 2019) and liver function(Rhee et al., 2017), but inclusion in pediatric models is hampered bythe lack of quantitative data on the pathophysiological processes (G. F.Li, Gu, Yu, Zhao, & Zheng, 2016; Rasool et al., 2015; Watt et al., 2018).This is also seen in studies where the effects of a reduction in bloodflowwere investigated, which at this stage could not bemechanisticallyincluded, because of pathophysiological differences in organ blood flowin children compared to adults (Emoto, Johnson, McPhail, et al., 2018;Rasool, Khalil, & Laer, 2016). Effects of genetic polymorphisms havebeen studied in pediatric PBPK models, often associated with changesin metabolizing enzyme activity (Ogungbenro & Aarons, 2015; Zakaria& Badhan, 2018). Similarly, genetic variation in transporters and targetreceptors can be incorporated (Hahn et al., 2018). If more physiologicaldata about specific patient groups becomes available, better individual-ized PBPK model predictions can be made allowing subclassificationwithin age groups, thereby reducing unexplained inter-individual vari-ability and paving the way for more individualized modeling.

4.5. PBPK-PD models

Determination of PD differences between adults and children is ofmajor importance to allow pediatric bridging studies for drugs in devel-opment. Introducing the relevant pharmacodynamic processes into pe-diatric PBPK models will be the next step to determine fully age-appropriated drug doses. The structure of many different PBPK modelsdeveloped so far is relatively uniform, however, pharmacodynamicmodules have their unique structure that is dependent on availableknowledge and the process that is being described. Most straightfor-ward is to compare drug concentrations with target thresholds in caseof antibiotics, although in a more complex model bacterial count overtime could be described (Mohamed, Nielsen, Cars, & Friberg, 2012;Thompson et al., 2019). There are relatively few examples of modelsthat have incorporated a more complex pharmacodynamic componentfor adults and children (Kechagia, Kalantzi, & Dokoumetzidis, 2015;Kuepfer et al., 2016; Moj et al., 2017; Smith, Hinderliter, Timchalk,Bartels, & Poet, 2014). An important aspect for future model develop-ment will be the inclusion of age-related drug effects instead ofconnecting an adult pharmacodynamic model to a pediatric pharmaco-kinetic component, for which more developmental ex vivo and clinicalresearch is clearly needed (Marshall & Kearns, 1999).

5. Physiologically-based toxicokinetic models

The sameprinciples for building a PBPKmodel are also appliedwhenassessing the kinetics of a toxic compound, referred to asphysiologically-based toxicokinetic (PBTK) models. In fact, thephysiologically-based kinetic modeling approach has been around intoxicology longer than in the field of pharmacology (Pelekis, Gephart,& Lerman, 2001). These type of models are usually developed for differ-ent animal species and subsequently translated to humans to predict in-ternal exposure as part of the risk assessment of a wider range ofchemicals, like pollutants (Emond, Ruiz, & Mumtaz, 2017; Tohon,Valcke, & Haddad, 2019), metals (Fierens et al., 2016; Kirman, Suh,Proctor, & Hays, 2017), pesticides (Lu, Holbrook, & Andres, 2010;Oerlemans et al., 2019) and industrial products (Edginton & Ritter,2009). Children have also been considered as a vulnerable group atrisk, as they may experience relatively higher exposures to chemicalsand/or bemore sensitive to harmful effects. For example, a higher expo-sure to bisphenol A was predicted in children, because of their lowerelimination capacity compared to adults and the potentially higherweight-normalized intake (Edginton & Ritter, 2009). A difficulty en-countered while predicting developmental toxicity is that comparedto PBPK models, human and particularly pediatric PBTK models oftenwill carry more uncertainty about the dose internalized, and hencehigher variability in predictions (Lu et al., 2010).

6. Quality control

The widespread adoption of PBPK modeling in pediatric drug devel-opment and personalized dosing, largely depends on the quality of themodels and their validity to accurately predict exposure. The varietyin claims made by authors on reliability of the model predictions canbe explained by differences in quality and certainty of model parameterestimates. Confidence in modeling results could be increased byperforming simulation with similar drugs/formulations (Johnson,Zhou, & Bui, 2014), or the ability to predict drug-drug interactions(Ogungbenro et al., 2015).

Assessing the quality of PBPKmodels is a complicated task due to thewide variety of different purposes for model use, difference in (mecha-nistic) complexity, the number of data that is available for building andverification, and heterogeneity in quality measures (Sager et al., 2015).For pediatric PBPK models this might be even more difficult as also de-velopmental processes need to be incorporated and validated. Depend-ing on the purpose of the PBPK model, its quality needs to be evaluatedaccordingly. In case a model is designed to replace clinical studies inchildren, confidence in model performance ideally should be high and

10 L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

parameter uncertainty low, whereas for exploringmechanistic hypoth-eses (e.g. age-related differences in ontogeny), a lower level of confi-dence in less essential parameters could be acceptable (EMA, 2016).To have more insight into model quality, the following key aspectsneed to be considered while evaluating PBPK models.

6.1. Mechanistic uncertainty

Uncertainty in themechanistic bases of themodel increases the pre-diction error, while translating processes affecting absorption, distribu-tion and elimination from adults to children. For example, thecontribution of different enzymes involved in drug metabolism can beunknown, or disease-mediated changes on physiological processes arenot quantitatively described (Johnson, Cleary, et al., 2018). This meansthat assumptions are required, of which the impact onmodel outcomesneeds to be evaluated by sensitivity analysis.

6.2. Scaling/fitting parameters

If (multiple) parameters are scaled, uncertainties in other parame-ters could be masked or compensated in case they are not uniquelyidentifiable. Apparently acceptable concentration-time profiles pro-duced by a model for one drug or (pediatric) population, will in thiscase not be reproducible for another drug or in another population(Calvier et al., 2018). Ideally, external datasets are needed to confirmthe validity of the scaled parameter in a learning-confirming cycle. Asa minimum requirement, biological plausibility of the scaled parametercould be evaluated.

6.3. Quality and quantity of in vivo data for verification

The number and quality of data for verification is variable, rangingfrom sparse or opportunistic data to dense clinical trial data. Modelsare generally accepted when they correlate to observed data even ifthey are sparse, although the latter requires extra caution if for examplea measured concentration-time profile is not available for comparisonwith simulated data (Sager et al., 2015).

6.4. Software package

Multiple software programs are used for modeling and some can beconsidered better validated, as they have been tested and employed bya large number of users. Currently available commercial software pro-grams for pediatric PBPK modeling include Gastroplus, Simcyp andPK-Sim. Gastroplus has historically focused on prediction of drug ab-sorption, and Simcyp is often used for prediction of DDI's, althoughareas of application have been extended. The open access software plat-form PK-Sim is another option for which no coding skills are required.Manually coded models are more prone to errors even after review,butmay bemore flexible in order to answer specific research questions.

6.5. Transparency

To gain insight into parameter certainty, ideally an overview of themodel parameters (drug-related as well as physiological parameters)is described along with the references from which they originated.

7. Regulatory applications

Because physiological and drug-specific parameters are includedseparately, PBPK models are suited to predict drug concentrations in apopulation, or for applications where extensive clinical pharmacoki-netic information is not yet available. Currently, of all PBPK modelsused in drug approval applications submitted to the FDA about 15%serve a pediatric purpose, which is more than seen for other “specialpopulations” like elderly, or patients with impaired renal or hepatic

function (Grimstein et al., 2019; Jamei, 2016). PBPK modeling in gen-eral, and pediatric PBPKmodeling in particular, is a relatively new disci-pline, but its application is increasing rapidly in the majority ofpharmaceutical companies. Examples are now available where clinicalstudies have been replaced or informed by pediatric PBPK simulation ef-forts (Shebley et al., 2018; Wagner et al., 2015). For example, modelshave been used to (1) set a starting dose in a clinical trial with eribulinin children and adolescents 6–18 years of age, (2) bridge from immedi-ate release to extended release quetiapine formulations in children andadolescents 10–17 years of age, and (3) inform deflazacort dose adjust-ments needed when given together with drugs that may cause interac-tions in children and adolescents 4–16 years of age (FDA, 2016; Johnsonet al., 2014; Shebley et al., 2018). Regulatory authorities have recog-nized thepotential of PBPKmodeling and guidelineswere issuedmainlyfocused onwhat information should be incorporated in the PBPKmodeldocumentation by pharmaceutical industry, which might further stan-dardize the process of model development in regulatory applications(EMA, 2016; FDA, 2018).

8. Future perspectives

PBPK modeling in children aids in predicting the pediatric pharma-cokinetics of drugs for which no or sparse data is available. Whereasmodel performance is currently more challenging for neonatal popula-tions, drugs metabolized by non-CYP enzymes, drug transporter sub-strates, and drugs which are orally absorbed, PBPK models arecontinuously improved and refined in a learn-and-confirm cycle bythe inclusion of more accurate model parameters.

One aspect that could further improve pediatric PBPK model devel-opment is to fill the gap in availability and quality of systems data. Forthis purpose, quantitative proteomics is an attractive technique to as-sess ontogeny patterns for absolute expression of drugmetabolizing en-zymes and transporters in different organs and the interplay with otherco-variates. This approach is especially suitable for pediatric popula-tions where a low number or size of samples is usually available. Asmultiple proteins can be quantified at the same time, correlation be-tween protein expression can also be considered (Achour, Barber, &Rostami-Hodjegan, 2014; Heikkinen, Lignet, Cutler, & Parrott, 2015).The number of studies in children is limited, pediatric PBPK modelingtherefore will require pooling of data, resources and knowledge. Thisalso includes combining tissue material, plasma and body fluid samplesfrom different institutions and biobanks to cover the full age range ofpediatric development.

In addition to obtaining better defined physiological parameters,there is a need for verification of models. Opportunistic sampling ofplasma and other body fluids or tissues would be a way to obtain mea-surements for drugs already on the market, which were not thoroughlyinvestigated previously (Hahn et al., 2019; Salerno, Burckart, Huang, &Gonzalez, 2019). Open access publishing and publication of raw datawill aid in making optimal use of already available clinical data. If it isstill not possible to obtain enough data, micro-dosing studies could pro-vide a good alternative, as long as saturation of enzymes and trans-porters is not expected at therapeutic doses. By administering a smallamount of a labeled drug (often 1/100 of usual dose) its pharmacokinet-ics can be determined, without the risks associated with potential toxiceffects (Mooij et al., 2017; Roth-Cline & Nelson, 2015). From amodelingperspective, good quality clinical data is extremely valuable for studyingthe ontogeny of system parameters in case biological samples are hardto obtain (e.g. blood brain barrier transporter expression).

Whereas in the coming years pediatric PBPK model improvementwill be focused on prediction in specific age groups and for children hav-ing co-morbidities by better capturing the underlying (patho)physio-logical parameters, an area of potential future benefit is the use ofPBPK modeling in personalized medicine. This will require even moredetailed information on demographic, genotypic, and phenotypic char-acteristics. Development of a “virtual twin” in which patient-specific

11L.F.M. Verscheijden et al. / Pharmacology & Therapeutics 211 (2020) 107541

features like, age, weight, height, gender, ethnicity, and genetics of drugmetabolizing enzymes/transporters are taken into account in PBPKmodels, will contribute to better personalized dosing and predictionswithin specific age groups. This will allow pediatric PBPK models tofind their way into clinical practice (Tucker, 2017).

9. Conclusion

The application of pediatric PBPK models have gained momentumover the last years, partly because their development has been stimu-lated by the increased interest of regulatory authorities in this “specialpopulation” and the obligation of investigating pharmacological differ-ences between children and adults. Different pediatric PBPK modelshave been developed for a wide variety of purposes, including substitu-tion of clinical studies. While uncertainty in some physiological param-eters is higher and less data might be available for model verification inchildren, pediatric PBPK models have become more robust and start toapproach the mechanistic basis seen in their adult counterparts. In thecoming years model quality and mechanistic basis will further improveby inclusion of more (reliable) physiological data, which will provide asound basis for pediatric model acceptance (Burckart & van denAnker, 2019). In this way, with concerted efforts of academia, PBPKmodel developers, industry and regulators, the use of this approachwill further expand and be applied to optimize drug development inthe pediatric population. The role of modeling and simulation in drugdevelopment will undoubtedly increase and particular effort shouldbe invested in the development of these models for children, to exploitthe enormous potential of this evolution also for the pediatricpopulation.

Declaration of Competing Interest

TNJ is an employee of Certara UK Limited and involved in the devel-opment of the commercial Simcyp PBPK model. LFMV, JBK, SNW andFGMR declare that there are no conflicts of interest.

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